When batch correction corrupts gene expression: uncovering distortions in correlation structures
Nourisa, J.; Passemiers, A.; Moreau, Y.; Raimondi, D.
Show abstract
Batch correction is essential for integrating datasets and enabling population-level insights into health and disease. Embedding-based approaches are among the most widely used solutions, but here we highlight a critical, overlooked limitation: these methods can distort feature-to-feature (e.g., gene-gene) relationships, potentially undermining downstream analyses. We investigate this issue and introduce a novel metric to quantify it.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction 97%
- The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell. 96%
- Mining hidden knowledge: Embedding models of cause-effect relationships curated from the biomedical literature 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data 95%
- SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection 95%
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.